The present disclosure relates to transferring the look of an image to another image. Sometimes it is desirable for an image to be made to look similar to one taken by a master photographer. For example, black and white photographs of California's Yosemite Valley by Ansel Adams use light and shadow to lend a surreal quality to his landscapes. Traditionally, one tries to adjust a target image so that it looks like a model image by manually manipulating the target image's histogram to alter the contrast or brightness of the target image. An image histogram is a distribution of brightness values for an image. For example, a color or black and white image has a brightness (e.g., intensity or luminosity) value associated with each pixel in the image. (Images in color spaces that do not support a brightness value can be converted to a color space that does.) Each pixel's brightness value is used to increment a counter for a histogram corresponding to the distribution of brightness values for the image. Another approach is to perform a histogram matching operation in order to make the target image histogram similar to that of a model image histogram. However, while these techniques adjust the contrast of the image, they fail to appropriately highlight or fade details in the image and hence the look is not matched.
Another approach uses a bilateral filter to split the model and target images each into a smooth base layer and a high frequency detail layer. A histogram matching is performed on the target base layer and detail layer against the corresponding layers of the model image. The modified target layers are then combined together to form the resulting target image. But this approach can be time consuming. Without instant feedback, users may be loathe to use such a solution. Moreover, this technique cannot easily be brought to bear on a real-time sequence of images such as a video feed.